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Found 22 papers, 4 papers with code
Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active Nematics
Deep learning-based optical flow (DLOF) extracts features in adjacent video frames with deep convolutional neural networks.
Solvent-Aware 2D NMR Prediction: Leveraging Multi-Tasking Training and Iterative Self-Training Strategies
Although significant progresses have been made in predicting one-dimensional (1D) NMR, two-dimensional (2D) NMR prediction via ML remains a challenge due to the lack of annotated NMR training datasets.
Graph Multi-Similarity Learning for Molecular Property Prediction
Additionally, previous multi-similarity approaches require the specification of positive and negative pairs to attribute distinct predefined weights to different relative similarities, which can introduce potential bias.
GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks
This under-exploration can be primarily attributed to the limited availability of comprehensive and well-curated carbohydrate-specific datasets and a lack of Machine learning (ML) pipelines specifically tailored to meet the unique problems presented by carbohydrate data.
Molecular Identification and Peak Assignment: Leveraging Multi-Level Multimodal Alignment on NMR
Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors.
Asymmetric Contrastive Multimodal Learning for Advancing Chemical Understanding
Through practical tasks such as isomer discrimination and uncovering crucial chemical properties for drug discovery, ACML exhibits its capability to revolutionize chemical research and applications, providing a deeper understanding of chemical semantics of different modalities.
Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation
The confounding factors, which are non-sensitive variables but manifest systematic differences, can significantly affect fairness evaluation.
Characterizing the Influence of Graph Elements
To fill this gap, we started with the simple graph convolution (SGC) model that operates on an attributed graph and formulated an influence function to approximate the changes in model parameters when a node or an edge is removed from an attributed graph.
Motif-based Graph Representation Learning with Application to Chemical Molecules
MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher-level node representations via multilayer perceptron and/or message passing in graph neural networks.
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph
As far as we know, by applying abstract algebra in statistical learning, this work develops the first formal language for general knowledge graphs, and also sheds light on the problem of neural-symbolic integration from an algebraic perspective.
Predicting the Stereoselectivity of Chemical Transformations by Machine Learning
Since late 1960s, there have been numerous successes in the exciting new frontier of asymmetric catalysis.
Graph-Graph Similarity Network
In this paper, we propose a Graph-Graph Similarity Network to tackle the graph classification problem by constructing a SuperGraph through learning the relationships among graphs.
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
Disparate impact has raised serious concerns in machine learning applications and its societal impacts.
Variance Regularization for Accelerating Stochastic Optimization
While nowadays most gradient-based optimization methods focus on exploring the high-dimensional geometric features, the random error accumulated in a stochastic version of any algorithm implementation has not been stressed yet.
A Deep Learning Approach for COVID-19 Trend Prediction
In this work, we developed a deep learning model-based approach to forecast the spreading trend of SARS-CoV-2 in the United States.
NagE: Non-Abelian Group Embedding for Knowledge Graphs
We demonstrated the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential for designing embedding models.
Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks
Deep neural networks (DNNs) can be huge in size, requiring a considerable a mount of energy and computational resources to operate, which limits their applications in numerous scenarios.
A Group-Theoretic Framework for Knowledge Graph Embedding
We have rigorously proved the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential for model design.
Detecting Topological Defects in 2D Active Nematics Using Convolutional Neural Networks
Active matter consists of active agents which transform energy extracted from surroundings into momentum, producing a variety of collective phenomena.
Context Dependent Modulation of Activation Function
This modification produces statistically significant improvements in comparison with traditional ANN nodes in the context of Convolutional Neural Networks and Long Short-Term Memory networks.
Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks
Our approach tries to capture the intrinsic properties of a DNN classifier and uses them to detect adversarial inputs.
IMMIGRATE: A Margin-based Feature Selection Method with Interaction Terms
Relief based algorithms have often been claimed to uncover feature interactions.
